title

idx_spi <- function(.scale, .dist, ...){
  ...
  data |>                            # data contain `prcp`
    aggregate(.var = prcp,            # step 1: temporal aggregation
              .scale = .scale)|>     #         aggregate `prcp` with time scale    
                                      #         `.scale` to create `.agg`, by default
    dist_fit(.dist = .dist,           # step 2: distribution fit
             .method = "lmoms",       #         using L-moment to fit `.dist`
             .var = .agg) |>         #         distribution on `.agg`
    augment(.var = .agg)              # step 3: normalising 
                                      #         find the normal density for `.agg`
}

idx_spei <- function(.scale, .dist, ...){
  ...
  data |>                                  # data contain `tavg` and `prcp`
    var_trans(                              # step 1: variable transformation
      .method = "thornthwaite",             #         using the thornthwaite function
      .vars = tavg, .new_name = "pet") |>  #         on `tavg` to create `pet`
    dim_red(diff = prcp - pet) |>          # step 2: dimension reduction 
    aggregate(                              # step 3: temporal aggregation
      .var = diff,                          #         aggregate `diff` with time scale
      .scale = .scale) |>                  #         `.scale` to create `.agg`
    dist_fit(                               # step 4: distribution fit
      .dist = .dist, .method = "lmoms",     #         using L-moment to fit `.dist`
      .var = .agg) |>                      #         distribution on `.agg`
    augment(.var = .agg)                    # step 5: normalising 
                                            #         find the normal density for `.agg`
}